In MimicBot, we observe that the design above destroys a relevant inductive bias in the network, more specifically the spatial correlations in the input. This results from the fact that the spatial channels are flattened and processed by the fully-connected layers that do not enforce a preservation of such precious information.
We therefore propose to use a convolutional approach, while we still inject global information from the non-spatial observation using the channel attention mechanism as presented in the next section.
At the top of Figure REF , the processing block of our network is presented.
The input is initially processed by \(C_{\text{init}}\) , which increase the number of channels to 128.
Four residual blocks [1]} then sequentially process the data. Each block contains three convolutions and 3 leaky-ReLu.
Before further processing is executed, the spatial input is concatenated to the output of the last residual block to retain it in the final decision making steps.
This information is further processed by three convolutions with channel attention, which are described in the next section.
The output of the last convolution with attention is of shape 17x17x28, corresponding to the 17 spatial actions.
This tensor is then flattened and concatenated with the computed non-spatial actions. Note that the inductive bias on the shape of the spatial input is preserved throughout the decision process.
